Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm

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Bibliographische Detailangaben
Zeitschriftentitel:	Energy Science & Engineering
Personen und Körperschaften:	Li, Xia, Cui, Tao, Huang, Kun, Ma, Xin
In:	Energy Science & Engineering, 9, 2021, 3, S. 330-342
Format:	E-Article
Sprache:	Englisch
veröffentlicht:	Wiley
Schlagwörter:	General Energy Safety, Risk, Reliability and Quality

Details
Zusammenfassung:	<jats:title>Abstract</jats:title><jats:p>Compressor stations, which usually consist of multiple compressors in parallel, are installed to power natural gas travel in pipelines. Compressor station optimization, which should be expressed as a mixed integer nonlinear programming (MINLP) problem, makes economic sense for the entire gas transmission system. However, it has often been simplified as a nonlinear programming (NLP) or mixed integer linear programming (MILP) problem in previous research. Most of existing solutions are based on discretization and a genetic algorithm (GA). This paper addresses the general MINLP problem for compressor station optimization without simplification; a novel hybrid intelligent algorithm is proposed to solve this problem. The proposed algorithm, DWOA, leverages advantages of the whale optimization algorithm (WOA) and differential evolution (DE). The proposed algorithm can balance exploration and exploitation to find the global optimal solution. An approach to handling constraints is also presented, where the original problem model is reformulated to be continuous by expanding the flow rate range of the compressor. A case study is performed to illustrate the performance of this approach. Results show that the continuous reformulated model is easier to solve, and DWOA produces a satisfactory solution that differs from theoretical results by only 1.61%. In addition, DWOA demonstrates better accuracy and stability than WOA, DE, and DE‐WOA, another hybrid algorithm. Therefore, this solution has the potential to promote comprehensive compressor station optimization.</jats:p>
Umfang:	330-342
ISSN:	2050-0505
DOI:	10.1002/ese3.821